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Last week I got on a call with a founder doing roughly $20M in revenue who had recently decided to cut his company’s headcount by about half. Reason #1, create a more nimble workforce; reason #2, hire more AI-native employees; and reason #3, reduce expenses.

I didn’t know his business well enough to tell him whether it was a good idea, but the timing of the call was as relevant as anything.

Because if you’ve been paying attention to the news over the past few weeks, you no doubt heard that HubSpot, where I used to be VP of Marketing, cut nearly 660 people. Last month, Uber cut 3,300 people, almost 10% of the company, and on Monday, rumors abounded that Netflix was planning a 5% reduction-in-force (RIF) as well.

Four companies, four different stories. I honestly couldn’t tell you which ones are really about AI. And I’d bet some of their own employees can’t either.

But AI has become the default explanation this year, whether it’s true or not. And apparently there’s a name for when it’s not true: AI washing. It’s when a company blames AI for cuts that are really about something else, pointing to AI tools that aren’t actually built yet.

Funnily enough, HubSpot went the other way. CEO Yamini Rangan said the cuts weren’t driven by AI efficiencies. Her story is that the company reorganized around customer outcomes first, and the cuts came out of that. That’s HubSpot’s own framing, and the stock’s had a rough year (down 43% YTD), so take that with a grain of salt.

As a startup founder or builder, you might be asking yourself, what does a public company’s layoff strategy have to do with a 40-person startup?

Honestly, not much. HubSpot, Uber, and Netflix answer to shareholders and a stock price. Your pressure comes from a closely managed P/L, or from runway, or from whether customers are still buying your product. (And it looks like, so far, the data backs that up. Peter Walker, who runs insights at Carta, says AI hasn’t increased layoffs at startups.)

Data aside, what I do think tends to happen is that growth-stage founders look at what big tech is doing, consciously or not, and begin asking themselves a certain set of questions. Things like:

These are all really good questions. The trap is answering them with a few layoffs and calling it “AI.”

AI is a label, not a reason.

Underneath it, there’s almost always one of these:

Mix these up, or put one reason’s label on another reason’s cut, and it’s probably AI washing.

Your team will see through it fast. Say “AI” when it’s really money, and the people who stay trust your next explanation a little less.

Most cuts are a blend. The founder I was speaking to wanted a capability swap and a speed play: he needed different skills, and he wanted fewer layers between him and the work. The savings were an added bonus.

What matters is which reason is driving the decision, because the driver sets the order. In a money cut, you cut first, because cash doesn’t wait. In a speed or capability cut, you hire the senior leader first, because that person should drive the redesign of the team.

That’s where my one piece of advice came in. When you’re cutting for skills and speed, you’re usually rebuilding a function: engineering, sales, marketing, whatever.

And there are basically three ways to sequence it:

He was set on cutting, so I told him to do #2. Honestly, #3 works too, as long as you’re okay with your new leader’s first big move being a layoff.

The one I’d avoid is #1.

Most great leaders want to build their own team. Hand a new VP or C-level hire a group they didn’t pick, and you either get a leader stuck with someone else’s choices, or one who starts rebuilding the minute they walk in.

Either way, you’ve made your new leader’s job harder before they’ve even started.

Hector Serrano built the paid ads program at Wispr Flow, and he walked through how he’d rebuild it from scratch in 30 days. It’s a 15-min video that’s pulled 1.5M views in four days, and it’s a rare paid-ads breakdown from somebody actually doing the thing. The short version: ads are creator content, and he talks about how to use Trybe, structure creator comp, and more.

Michael Bloch boils defensibility down to one filter: is your moat hard to do, or hard to get? AI is making “hard to do” (software, integrations, switching costs) close to worthless, while “hard to get” (users, licenses, capital, physical infrastructure) gets stronger, because no model can compress years. The main moat I’d argue he’s missing — but he alludes to — is the trust + taste category.

Kyle Poyar breaks down why tech swag got cool again, from Palantir’s limited drops that sell out in 15 minutes to OpenAI’s mostly sold-out merch store. The playbook is scarcity over volume and a point of view over a logo, which is the opposite of the branded fleece sitting in your closet right now. It’s a fun read, and a sneaky-good lesson in brand when everything else is getting commoditized.

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Hope everyone has a great weekend.

And until next time, thanks for reading.

Jordan